An accelerated optimization algorithm for distributed model predictive control

Abstract

We regard a network of systems with coupled dynamics or constraints that we want to controloptimally. The costfunction is assumed to be separable and convex. We show that the corresponding centralized model predictive control (MPC) problem can be recast as a separable convex optimization problem. Furthermore, we present a dual-based decomposition method, called here the proximal center method, to solve separable convex problems. In Necoara and Suykens (2008) we have provided convergence proofs and eficiency estimates for the proximal center method which improves with one order of magnitude the bounds on the number of iterations of the classical dual subgradient method. The new distributed optimization method is suitable for application to distributed MPC since it is highly parallelizable, each subsystem uses local information and the coordination between the local MPC controllers is performed via the Lagrange multipliers corresponding to the coupled dynamics. Simulation results are also included.